Sensing and Field Data Capture for Construction and Facility Operations

Collection of accurate, complete, and reliable field data is not only essential for active management of construction projects involving various tasks, such as material tracking, progress monitoring, and quality assurance, but also for facility and infrastructure management during the service lives of facilities and infrastructure systems. Limitations of current manual data collection approaches in terms of speed, completeness, and accuracy render these approaches ineffective for decision support in highly dynamic environments, such as construction and facility operations. Hence, a need exists to leverage the advancements in automated field data capture technologies to support decisions during construction and facility operations. These technologies can be used not only for acquiring data about the various operations being carried out at construction and facility sites but also for gathering information about the context surrounding these operations and monitoring the workflow of activities during these operations. With this, it is possible for project and facility managers to better understand the effect of environmental conditions on construction and facility operations and also to identify inefficient processes in these operations. This paper presents an overview of the various applications of automated field data capture technologies in construction and facility fieldwork. These technologies include image capture technologies, such as laser scanners and video cameras; automated identification technologies, such as barcodes and Radio Frequency Identification (RFID) tags; tracking technologies, such as Global Positioning System (GPS) and wireless local area network (LAN); and process monitoring technologies, such as on-board instruments (OBI). The authors observe that although applications exist for capturing construction and facility fieldwork data, these technologies have been underutilized for capturing the context at the fieldwork sites as well as for monitoring the workflow of construction and facility operations.     

Automated Generation of Customized Field Data Collection Templates to Support Information Needs of Cost Estimators

While estimating activity production rates, cost estimators rely on historical production rates. To have realistic and useful cost estimates based on historical production rates, such production rate data should be augmented with historical contextual information that depict conditions under which activity production rates were achieved in past projects. This information is needed in determining which production rate to use among alternates for a similar activity existing in a new bid. Estimators need contextual information especially when they are unfamiliar with the work being estimated. Hence, such information items need to be identified, collected, and stored for estimators’ use in new projects. This paper details a construction-method specific and an extensible approach that is developed for enabling cost estimators to define contextual information items that need to be collected on job sites and stored as part of project histories. Based on this approach, the writers implemented a prototype system, called as ContextGen, and performed user-tests with estimators with different experience levels. Results showed that the developed approach captures method-specific information needs of estimators and is extensible to incorporate new contextual information items that can have different data representations. The developed approach is also precise in retrieving contextual information items specific to a construction method from a set of predefined contextual information items available in a library.     

Interpretation of Automatically Monitored Lifting Equipment Data for Project Control

Interpretation of the data that can be collected by automated monitoring systems on construction sites is the most significant challenge to providing useful management information. Distinct construction operations must be identified and associated with construction activities, so that they can be related to construction plans. Earlier research has indicated that construction equipment can be monitored conveniently and that individual equipment operations can be isolated and characterized. In this work, an approach has been developed for unique association of isolated equipment operations with planned construction activities. The approach is based on comparison of the values of various characteristics, calculated for each equipment operation, against preset filters of characteristic values for all expected basic construction activities. The composition of the set of characteristics is different for each data stream monitored and is dependent on the nature of the construction activities. The method has the distinct advantage of ensuring the uniqueness of each filter within the collection of filters when the system is calibrated at the start of any project, rather than during online data processing. In this way, rapid and accurate interpretation of monitored data can be guaranteed. The method was tested using data collected during construction of a high-rise office tower.     

Framework for Providing Customized Data Representations for Effective and Efficient Interaction with Mobile Computing Solutions on Construction Sites

Conceptual representations of information contained in product and process models are often difficult to use for accessing data when performing engineering tasks. This is especially true if project-management information contained in product and process models needs to be made accessible on a mobile computer on construction sites. To make this information accessible, customized conceptual and visual information representations are needed. For the project-management tasks of progress monitoring and creating and administering punch lists, existing approaches that provide access to relevant project information are ineffective and inefficient in transforming information from product and process models into usable representations. As a result, these applications do not always provide information representations that are of the required structure, granularity, and type. In this paper, we describe a navigational model framework, which is an approach that effectively and efficiently creates and manages different views of information contained in product and process models. We validated this framework by implementing a prototype system and testing it through a designed set of experiments. The use cases for these experiments were established in an extensive study on the information and data collection needs on construction sites.     

Measuring and Modeling Labor Productivity Using Historical Data

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.     

As-Built Information Model for Bridge Maintenance

The transportation infrastructure is key to economic development in the United States. Providing a high level of serviceability through periodic inspection and maintenance is important in keeping the transportation system operational and in avoiding major replacement efforts. Of particular importance is the inventory of bridges in the national transportation infrastructure, due to their high cost and direct impact on public safety. The focus of this paper is on information management in support of bridge maintenance functions. Particularly, the research project discussed in the paper addresses the need for inclusion of construction as-built data in the bridge management database along with the periodic inspection and maintenance data. Attention to this type of data has been lacking. Therefore, the paper promotes bridge as-built data, discusses its role in bridge management, and demonstrates the proper design of an as-built information management model and system that is integrated with existing standard bridge management systems such as Pontis.     

Data-Fusion Approaches and Applications for Construction Engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.     

Characterization and Search of Construction Inspection Plan Spaces Developed Using a Component-Based Planning Approach

Formal construction inspection planning is needed to help reduce the incidence of overlooked or inefficient inspections, and to help realize the potential of emerging sensing technologies. Prior publications by the authors have presented requirements for such an approach and a component-based inspection planning implementation as an approach in addressing some of the requirements towards formal construction inspection planning. Implementation of such a component-based approach for typical construction examples, however, leads to the generation of large, rugged search spaces. This paper first describes the characteristics of search spaces that are generated using a component-based approach to illustrate which search mechanisms are appropriate to explore these search spaces. The paper then describes a set of search algorithms and heuristics that were investigated and evaluated for searching construction inspection plans. Specific discussions on how these algorithms performed in searching within inspection planning spaces are provided based on experiments conducted using a testbed characterizing a construction site and a building performance monitoring decision-making example.     

Contextual Information Requirements of Cost Estimators from Past Construction Projects

Past project data sources provide key information for construction cost estimators. Previous research studies show that relying only on one’s own experience during estimation results in estimators’ bias. Having and referring to historical databases, containing objective information on what happened in past projects, are essential for reducing estimators’ biases. The first step toward development of useful project history databases is to understand what information estimators require from past projects. The research described in this paper targets estimators’ information needs identified through interviews, brainstorming sessions, task analyses, and card games conducted with estimators with different experience levels and specialized in heavy/civil and commercial construction projects, and exploration of historical and standard databases available in companies to determine what is being currently represented. Findings show that estimators need contextual information, depicting the conditions under which specific production rates were achieved, so that they can identify which production rate would be more realistic to use during the production rate estimation of an activity in a new bid. Comparison of the contextual information needs identified in this research with information items available in historical data sources (such as company cost reports, RSMeans, previous studies) highlighted some gaps and important opportunities for improvements in those sources. The identified contextual information items are significant for practitioners in developing ways to augment their existing project history databases to make them more beneficial for estimators.     

Assessing Residual Value of Heavy Construction Equipment Using Predictive Data Mining Model

Construction equipment constitutes a significant portion of investment in fixed assets by large contractors. To make the right decisions on equipment repair, rebuilding, disposal, or equipment fleet optimization to maximize the return of investment, the contractors need to predict the residual value of heavy construction equipment to an acceptable level of accuracy. Current practice of using rule-of-thumb or statistical regression methods cannot satisfactorily capture the dynamic relationship between the residual value of a piece of heavy equipment and its influencing factors, and such rules or models are difficult to integrate into a decision support system. This paper introduces a data mining based approach for estimating the residual value of heavy construction equipment using a predictive data mining model, and its potential benefits on the decision making of construction equipment management. Compared to the current practice of assessing equipment residual values, the proposed approach demonstrates advantages of ease of use, better interpretability, and adequate accuracy.     

Embedded System for Construction Asset Tracking Combining Radio and Ultrasound Signals

Today’s sensor technology provides the increased opportunities for automation and improvement in data acquisition and construction processes. However, many current field practices at construction sites still rely on manual processes for asset tracking and information handling. Previous technologies, such as radio frequency identification and global positioning systems, do not provide a solution to automated asset tracking because of their limitations in terms of applicability and performance in a typical construction environment. This paper introduces a new development of an embedded sensor system for construction asset tracking by combining radio and ultrasound signals. We present the detailed hardware and software architecture and have implemented outdoor experiments to examine the accuracy and performance of the designed system. The results obtained showed the accurate distance and position estimation with enhanced networking flexibility. The findings and lessons learned from this research demonstrate the potential for future practical deployment of similar systems in many civil engineering applications.     

Web-Vacuum: Web-Based Environment for Automated Assessment of Civil Infrastructure Data

Data quality is extremely important where information dramatically influences the decisions being made. In the context of civil infrastructure systems, planning and management activities are critically dependent on data to support the efficient allocation of resources, detailed cost-benefit analysis, and informed decision-making. A Web-based tool, called Web-Vacuum, which employs data-mining (DM) techniques and partially implements a two-level data-quality assessment procedure, was developed to support the general purpose of data-quality assessment. The algorithms, workflow, and interfaces used in Web-Vacuum are presented. A data-quality assessment case study using a bridge management system data set is used to demonstrate that the application of Web-Vacuum can be used to assist in determining the quality of a data set.     

Construction Project Risk Assessment Using Existing Database and Project-Specific Information

This paper develops a risk assessment methodology for construction projects by combining existing large quantities of data and project-specific information through updating approaches. Earlier studies have indicated that risk assessment is still difficult for practicing engineers to use due to the requirement of data on too many input variables. However, the availability of existing large quantities of data and project-specific information makes it possible to simplify the risk assessment procedure. Two main ideas are pursued in this paper to facilitate practical implementation: identify and evaluate the critical risk events, and develop a systematic updating methodology. Both epistemic and aleatory types of uncertainties in the data are considered, and corresponding updating procedures are developed. The proposed methodology is illustrated for the construction risk assessment of a cable-stayed bridge.     

Identification of Natural Frequencies and Dampings of In Situ Tall Buildings Using Ambient Wind Vibration Data

An accurate prediction for the response of tall buildings subject to strong wind gusts or earthquakes requires the information of in situ dynamic properties of the building, including natural frequencies and damping ratios. This paper presents a method of identifying natural frequencies and damping ratios of in situ tall buildings using ambient wind vibration data. Our approach is based on the empirical mode decomposition (EMD) method, the random decrement technique (RDT), and the Hilbert–Huang transform. Our method requires only one acceleration sensor. The noisy measurement of the building acceleration is first processed through the EMD method to determine the response of each mode. Then, RDT is used to obtain the free vibration modal response. Finally, the Hilbert transform is applied to each free vibration modal response to identify natural frequencies and damping ratios of in situ tall buildings. The application of the proposed methodology is demonstrated in detail using simulated response data of a 76-story benchmark building polluted by noise. Both the along-wind and across-wind vibration measurements have been illustrated. Simulation results demonstrate that the accuracy of the proposed method in identifying natural frequencies and damping ratios is remarkable. The methodology proposed herein provides a new and effective tool for the parametric identification of in situ tall buildings.     

Automation and Robotics for Construction

The potential for automated real‐time data acquisition, process control and robotics for remote, large‐scale field operations, such as those on construction engineering projects, is addressed. Classifications of technologies for automation and robotics in such operations include hard‐wired instrumentation, remote sensing, analog and digital telecommunications, optical (laser, infrared and fiber‐optic) data transmission, monitoring via microcomputer‐based instrument control and data recording, on‐site process control for fixed plants, partial or fully automatic control of mobile equipment, fixed‐based manipulators, mobile robots, communications between on‐site computers and automated machinery, electronic ranging and detection, and video‐image pattern recognition. Combining selected technologies with microcomputer‐based software could facilitate analysis, design and control decision‐making, and could provide a means of coordinating various discrete automated components or machines that must work together to perform field tasks. This paper also mentions categories of needs for such technologies on field operations, and potential barriers to implementation. Progress will depend on the interest and support of researchers qualified to advance this field.     

Reduction of Short-Interval GPS Data for Construction Operations Analysis

The systems that historically have been used to collect data for time studies of construction operations are manual in nature and limited to the observer’s field of view. Global Positioning System (GPS) technology incorporated into an onboard instrumentation system can be used to autonomously collect position and velocity data without the field of view limitation. Data must be collected at a short time interval to provide the level of detail necessary for operations analysis. Thus the issue becomes managing the data and identifying the relatively key records that mark the start and stop of activities. A field observer identifies the key times in real time with instantaneous decisions of when one activity stops and the next starts based on enormous volumes of visual information. This work developed a methodology for making equivalent decisions based on GPS data and presents the procedures developed to identify the key records necessary to calculate activity durations. A case study is used to illustrate application of the system to an earthmoving operation. Also, it is postulated how the information can be used in discrete event simulation.     

Java Inspection Framework: Developing Field Inspection Support Systems for Civil Systems Inspection

In order to provide useful and practical computing support for inspectors in the field performing inspection activities, computer systems have to be designed and customized to recognize the specific task and context of the inspection. Unfortunately, the development of field inspection support systems is difficult and still suffers from a lack of more general design knowledge. Effort is wasted when implementing different applications that share some common aspects. This paper presents a partial solution to the difficult problem of developing field inspection support systems—a modular and customizable software environment to facilitate the construction of such applications.     

Ontology-Based Approach to Context Representation and Reasoning for Managing Context-Sensitive Construction Information

The construction industry is an information-intensive industry and heavily relies on documents, including physical and virtual documentation and models, to exchange context-sensitive information among different project participants. Many research efforts have been made to help manage construction information; however, few of them considered the context-sensitive nature of the information. In this paper, the writers propose a new approach to facilitate the management of context-sensitive construction information that is stored in different textual documents. The approach addresses the context-sensitive nature of construction information by representing contexts in ontologies and by using contexts as indices of the information. The approach also presents a reasoning mechanism that leverages the semantically rich features of ontologies to reason about contexts to evaluate their applicabilities. Two case studies were conducted, and the results showed the proposed approach can effectively retrieve, classify, and manage construction information. Finally, the writers discuss the limitations of the proposed approach and future research directions.     

Construction Scheduling and Progress Control Using Geographical Information Systems

Traditional scheduling and progress control techniques such as bar charts and the critical path method (CPM) fail to provide information pertaining to the spatial aspects of a construction project. A system called PMS-GIS (Progress Monitoring System with Geographical Information Systems) was developed to represent construction progress not only in terms of a CPM schedule but also in terms of a graphical representation of the construction that is synchronized with the work schedule. In PMS-GIS, the architectural design is executed using a computer-aided drafting (CAD) program (AutoCAD), the work schedule is generated using a project management software (P3), the design and schedule information (including percent complete information) are plugged into a GIS package (ArcViewGIS), and for every update, the system produces a CPM-generated bar chart alongside a 3D rendering of the project marked for progress. The GIS-based system developed in this study helps to effectively communicate the schedule∕progress information to the parties involved in the project, because they will be able to see in detail the spatial aspects of the project alongside the schedule.     

Highway Construction Data Collection and Treatment in Preparation for Statistical Regression Analysis

Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration. The purpose of this work is to describe the highway construction data needed for such a study, identify a data source, collect early-design project data, and prepare the data for statistical regression analysis. The Virginia Department of Transportation is identified as the optimal data source. The data collected include historical contract and project level parameters. To prepare for statistical regression analysis, the contract duration collected is converted to construction duration by a seasonal adjustment process which removes historically typical nonworking days.